iBKH-based Link Prediction

This module enables link prediction in iBKH, i.e., predicting entities that potentially link to the target entity (input).

Method detail

  • Step I. Knowledge graph embedding. We use the Deep Graph Library - Knowledge Embedding (DGL-KE) (https://github.com/awslabs/dgl-ke) [1], a Python-based implementation for the advanced KGE algorithms, such as TransE [2], TransR [3], ComplEx [4], and DistMult [5].
  • Step II. Link prediction based on the learned embedding vectors of entities and relations.


  • Specify the Entity Type and Entity Name of the target entity of interest (e.g., Alzheimer’s disease entity [Entity Type = Disease, Entity Name = Alzheimer’s disease ]) in the left sidebar.
  • Click Submit to run inference.

It will return lists of drugs, genes, symptoms, and pathways which are most likely to link to the target entity. Such procedure may take minutes.

An example of knowledge discovery for Alzheimer’s disease:


  1. Zheng D, Song X, Ma C, Tan Z, Ye Z, Dong J, Xiong H, Zhang Z, Karypis G. Dgl-ke: Training knowledge graph embeddings at scale. InProceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval 2020 Jul 25 (pp. 739-748).
  2. Bordes A, Usunier N, Garcia-Duran A, Weston J, Yakhnenko O. Translating embeddings for modeling multi-relational data. Advances in neural information processing systems. 2013;26.
  3. Lin Y, Liu Z, Sun M, Liu Y, Zhu X. Learning entity and relation embeddings for knowledge graph completion. InTwenty-ninth AAAI conference on artificial intelligence 2015 Feb 19.
  4. Trouillon T, Welbl J, Riedel S, Gaussier É, Bouchard G. Complex embeddings for simple link prediction. InInternational conference on machine learning 2016 Jun 11 (pp. 2071-2080). PMLR.
  5. Yang B, Yih WT, He X, Gao J, Deng L. Embedding entities and relations for learning and inference in knowledge bases. arXiv preprint arXiv:1412.6575. 2014 Dec 20.